Automatic Extraction of Skin and Soft Tissue Infection Status from Clinical Notes.
Stud Health Technol Inform
; 310: 579-583, 2024 Jan 25.
Article
en En
| MEDLINE
| ID: mdl-38269875
ABSTRACT
The reliable identification of skin and soft tissue infections (SSTIs) from electronic health records is important for a number of applications, including quality improvement, clinical guideline construction, and epidemiological analysis. However, in the United States, types of SSTIs (e.g. is the infection purulent or non-purulent?) are not captured reliably in structured clinical data. With this work, we trained and evaluated a rule-based clinical natural language processing system using 6,576 manually annotated clinical notes derived from the United States Veterans Health Administration (VA) with the goal of automatically extracting and classifying SSTI subtypes from clinical notes. The trained system achieved mention- and document-level performance metrics of the range 0.39 to 0.80 for mention level classification and 0.49 to 0.98 for document level classification.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Infecciones de los Tejidos Blandos
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Límite:
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos